However, not all smartphone owners use their device in the same way, and it’s hard to believe that the occasional, casual use of the mobile phone has the same kind of negative effects as high-intensity use.

In this work, we uncover broad, latent patterns of mobile phone use behavior. To ensure that we do not introduce any kind of bias by pre-defining types of use, we employed an unsupervised learning method to identify, without our intervention, the most common types of phone usage.

We conducted a large-scale study where, via a dedicated logging app, we collected daily mobile phone activity data from a sample of 340 participants for a period of four weeks. The goal of the study was to explore two research questions:

What are the most common, high-level types of mobile phone use?

Will mobile phone use associated with negative well-being stand out by itself?

Clusters of Mobile Phone Use

Through an unsupervised learning approach and a methodologically rigorous analysis, we reveal five generic phone use profiles which describe at least 10% of the participants each:

Limited Use

Members of this cluster scored low in almost all usage categories. They tended to keep their ringer mode in the normal setting, indicating that they were not too bothered by calls and notifications. We used this cluster as a baseline to compare the other clusters against.

Business Use

Members of this cluster stood out by their significantly more frequent use of phone calls – incoming and outgoing. While phone calls are comparably frequent, members of this cluster have comparably fewer nightly use sessions and app launches. The ringer mode is typically set to normal, indicating that hearing the phone is important to them. We labeled the cluster as business use, since we associated phone calls with business activity.
In terms of well-being, members of this cluster did not stand out much. Only during the weekend, they were found to report significantly lower tense-arousal. When accounting for night-time and day of the week, we found that during the weekend, members of the cluster reported higher levels of boredom than the baseline cluster.

Power Use

Members of this cluster stood out by their increased session duration & number of nightly sessions, battery use, and mobile data use. During the day, they launched a significantly higher number of email-, game-, and to a lesser extent social media apps. Messaging apps, in contrast, are used less often during the day compared to other clusters. During the night, members of this cluster show the highest use of email apps and a somewhat increase use of messaging apps. While the ringer mode is typically set to normal mode, members of this cluster had the highest variance in ringer mode setting.
Despite the high level of mobile phone use, members of the Power Use cluster do not stand out negatively in any of the well-being related factors. Compared to the baseline, they are more awake during the weekend and report lower levels of boredom during the night. Compared to Cluster 4 mentioned below, they scored lower in terms of depression (PHQ-8) and neuroticism (Big5).

Personality-induced Problematic Use

Members of this cluster stood out by an increased number and length of sessions during night time, and an increased use of email and messaging apps during the night. In addition, another characteristic behaviour was that they typically set the ringer to silent mode. Members of this cluster scored worst in terms of well-being.
Compared to the baseline cluster, they reported significantly higher levels of tense-arousal, boredom, and lower valence. In contrast, when accounting for night-time and non-working days, the significant differences regarding tense-arousal and valence disappeared. Boredom, however, was significantly lower during night-time. These findings indicate a tendency towards experiencing more stress and boredom during working hours. Members of this cluster further tended to be more neurotic / less emotionally stable than members of other clusters. Finally, members of this cluster scored significantly higher on the PHQ-8 questionnaire than Limited and Power Users, indicating a tendency towards experiencing depression-related symptoms.

Externally-induced Problematic Use

Like the previous cluster, members of this cluster tended to have more and longer phone use sessions during night-time. The main difference to Cluster 4 is that during night-time, only the use of messaging apps is comparably higher. In contrast, the use of email apps is lower. Finally, the ringer mode of these users is typically set to vibrate.
There were significant effects of membership of this cluster on the emotional self-reports. Compared to the baseline cluster, its members reported significantly higher tense arousal, lower energetic arousal, lower valence, and higher levels of boredom. During night-time, however, energetic arousal was significantly lower – an indication of being more tired – and the other effects subsided. During the weekend, tense arousal was significantly lower, valence was significantly higher, and significant effects on energetic arousal and boredom disappeared.
Also, members of this cluster scored significantly higher in terms of emotional stability compared to the baseline cluster as well as Cluster 4. Further, PHQ-8 depression scores were significantly higher than those of the baseline cluster, however, the effect was not as pronounced as in Cluster 4.
While members of this cluster tended to be stressed during working time, they seem to better compensate during non-working hours: they are being tired during the night and happy during the weekend. This finding is corroborated by higher emotional stability. We interpret the main difference that members of this cluster have a stressful daytime, but are more affected by external factors rather than internal factors, and therefore better cope with the stressful weekdays.

Summary

Intuitively, people tend to associate power use with negative outcomes. However, the data provided by our work does not support this simplistic conclusion. Instead, we found evidence that intense mobile phone use alone does not predict negative well-being. Instead, our approach automatically revealed two groups with tendencies for lower well-being, which are characterized by nightly phone use sessions.

In 2014, I presented at ACM MobileHCI the results of an in-situ study, where we observed in detail, what types of notifications 15 mobile phone users receive and how they handle them. One of the key findings, that keeps being cited in other works, is that people receive an average of 63.5 notifications per day. However, since this statistics was derived from a sample size of 15 people, I never felt too confident about it. I always wanted to validate these statistics on a much larger sample.

The opportunity came in the form of a data set, that we collected as part of a study to explore moments to engage with mobile phone users. Together with my intern Amalia Vradi and my colleague Souneil Park, we analyzed 794,525 notifications from 278 mobile phone users and how they were handled.

Our participants received a median number of 56 notifications per day, which does not indicate that the number of notifications has increased over the past years.

We identified 5 different groups of notifications and explored them separately:

messaging (e.g. WhatsApp individual messages)

group messaging (e.g. WhatsApp group messages)

email

social networks (e.g. Facebook likes)

non-social networks (e.g. Dropbox)

Comparing these groups, we found that messaging apps create most of the notifications and are the only app type where with a conversion rate of about 65%, notifications are reasonably effective.

Notifications from other types of notifications are not really effective rarely lead to a conversion (rates between ca. 15 and 25%). A surprisingly large fraction of notifications is received while the phone is unlocked or the corresponding app is in foreground, hinting at possibility to optimize for this scenario. Finally, we show that the main difference in handling notifications is how long users leave them unattended if they will ultimately not consume them.

Many of today’s mobile software products and services, such as games, brands, social networks, or news feeds, need to engage their users in order to be successful, where engagement refers to the involvement into something that attracts and holds our attention.

Failing to engage users can endanger the sustainability of products and services, particularly if they are free to use and cover their costs through secondary streams of income, such as advertisements or upsells, which require repeated use of the service. However, engaging mobile users is increasingly challenging as we are exposed to an ever-growing number of online products and services which are all competing for our attention.

Many of today’s mobile products and services engage their users proactively via push notifications. However, such notifications are not always delivered at the right moment, therefore not meeting products’ and users’ expectations. Notifications delivered at the wrong moment or to uninterested users may even lead to churn.

To address this challenge, we aim at developing an intelligent mobile system that automatically and continually infers, whether a user would be open to engage with suggested content in each moment.

To inform the development of such a system, we carried out a field study with 337 mobile phone users. For 4 weeks, participants ran a study application on their primary phones. They were tasked to frequently report their current mood via a notification-administered experience-sampling questionnaire.

However, for this study, we were interested in whether they voluntarily engaged with content that we offered at the bottom of that questionnaire. In the informed consent, we had clearly communicated that interacting with this content is voluntary. Hence, our participants never felt required to interact with it.

For the prediction of whether participants would interact with this content, we used a wide range of data related to their mobile phone use, such as the time that the screen was last turned on, the current activity (walking, still, cycling, …), or the amount of data consumed during the last 60 minutes.

On the basis of 120 Million of such phone-use events and 78,930 questionnaire notifications, we build a machine-learning model that — just before delivering a questionnaire notification — predicts whether a participant will not only click on the notification, but also subsequently engage with the content offered at the bottom of the questionnaire.

When compared to a naïve baseline, which emulates current non-intelligent engagement strategies, our model achieves 66.6% higher success rate in its predictions. If the model also considers the user’s past behavior, predictions improve 5-fold over the baseline, while avoiding to failed engagement attempts with about one-third of the participants.

Such a classifier could be used in products to increase conversion rates, improve user experience, and lower churn by reducing the number of undesired interruptions.

Push notifications are increasingly being used to engage mobile device users with app content. News organizations deliver breaking-news notifications, social platforms inform about new content, games inform about status updates game, etc … with the goal of making the user engage with the service.

In this research, we – Kostadin Kushlev, University of Virginia, Bruno Cardoso, KU Leuven, and myself – explored to what extent users’ current affect, that is, how they are feeling, impacts user engagement. To this end, we analyzed data from a study conducted by Telefónica Research where the participants (N = 337) downloaded a custom-developed app that delivered notifications.

After attending to a notification (N = 32,704), participants reported how they felt in a mini questionnaire. Besides asking how the participants felt, the questionnaire also offered them to voluntarily engage with further content. Participants were not aware that we our main interest was in observing their interaction with said content — they believed that it was mainly there as a courtesy to make their participation in the study more fun.

Participants always had two choices: a mentally demanding and a simple/diverting task. The tasks in these groups were chosen from a list of 4 options each. The mentally demanding offers included: browsing trending games on Google Play, reading the Wikipedia article of the day, filling out a personality questionnaire, or playing a thinking game. The simple and diverting option included watching a trending video, reading fun facts, playing an action game, and watching trending gif images.

The results show a clear impact of affect on the choice of the content:

When feeling good, people tend to avoid mentally demanding tasks. Hence, proactive recommendations for content that requires mental effort should target moments of neutral or even negative valence.

When tense, people tend to avoid diverting tasks. Thus, people who want to reduce task-induced stress might want to rely on external timers to schedule regular breaks with fun activities.

When energetic, people tend to avoid suggestions for further distraction altogether. Hence, proactive recommendations should target moments of low energetic arousal, such as moments of boredom.

These findings show that the current emotional state affects the kind of content users choose to engage with. Future “smart” devices should not only be technologically smart, but also psychologically smart. They should strive to understand how users feel in order to engage them with the most appropriate content at the most opportune of times.

The Do Not Disturb Challenge

We asked 30 volunteers to disable notification alerts for 24 hours across all devices and all services. We carefully walked the participants through all devices and services. Where possible, we used system-wide settings, such as the Do Not Disturb mode, to suppress all alerts. In other cases, such as Skype, we showed people how to disable notifications in the settings of the respective app. Please note that participants could still read messages or emails, they would simply not receive any alert.

To study the effect that notifications have on us, we captured self-reported feedback, and compared it to the same self-reported feedback collected via questionnaire during a normal baseline day. Furthermore, after the study, we conducted a post-hoc interview to uncover themes that we had not anticipated. We discovered the following main effects.

Drop in Engagement and Reduced Responsiveness

The absence of notifications had a significant effect on how participants perceived their engagement with the mobile phone. For example, Participant #02 “forgot my phone at work” because of not being reminded of the phone by notifications.

Increased Productivity

As expected, notifications distract. Hence, the answers of the questionnaire show that participants felt significantly less distracted and more productive: Participant #07 said that it was “easier to concentrate, especially when working on the desktop (computer).”

Lack of notifications caused to miss information

During the day without notifications, participants were significantly more likely to agree with the statements that they missed professional or personal information. During the post-hoc interview, we collected several anecdotes. For example: because of the lack of notifications, Participants #12 forgot to continue a chat with a friend. As a consequence, this friend got angry for not receiving replies.

Lack of notifications induced worry

Consequently, participants were significantly more likely to agree to the statement “I felt worried about missing notifications“. For example, Participants #04 “was meeting with [a friend] for lunch, and I knew that I was going to receive something from her“.

More frequent checking of the phone

During the day without notifications, agreement to the statement “I frequently turned on the phone to check for missed notifications“. For example, Participant #12 stated that “because of the reaction of my friend, who got angry because I forgot to respond, I was the whole afternoon with phone in my hand.”

Stress

Interestingly, there were no significant effects on the two stress related items, neither on “I felt stressed” nor on “I felt relaxed“. This might be explained by the finding that there are two opposing stress-inducing effects at work — stress from the interruptions and stress from being anxious to miss important information or violate expectations –, which influenced participants to different extents.

Reduced feeling of social connectedness

Our study revealed a link between notifications and staying emotionally in touch with one’s social group. During the day without notifications, agreement to the statement: “I felt connected with my social group” was significantly lower. These results contrast that — while work-wise, disabling notifications helped to be more focused and productive — socially, they negatively affect the feeling of being in touch with one’s social group.

Polarized reactions to being without notifications

The participants’ post-study reflections to having notifications disabled varied greatly. They ranged from very positive responses, such as “It was amazing! I felt liberated! (Participant #22) over neutral responses, such as “It was not a big deal, since I am usually not checking notifications and people know that I am not responsive” (Participant #25) to very negative responses, such as “I was paranoid and I even left the screen on not to miss a friends notification“} (Participant #04).

The main predictor for the participants’ attitude that we observed was to what extent others typically expected them to respond quickly to messages: the faster the usual response, the more negative the experience.

Signs of notifications overload

For more than two-third of the participants, the participation in the Do Not Disturb Study caused them to reflect on their notifications usage. Almost half of the participants stated the plan to use Do Not Disturb or similar similar notification-suppression modes in the future. For example, Participants #24 realized that “when I need to really get things done, I need to turn notifications off.”

One third stated the plan to manage notifications more consciously. For example, Participants #20 was “considering to only keep notifications for the important things, so people can better reach me“. Participants #26 had come to the conclusion that the “important apps are Messenger, Hangout and WhatsApp.” This shows how important instant messaging has become: people depend on notifications to maintain the expected level of responsiveness. This also shows that – despite the negative effects of notifications – disabling them altogether is not an option.

Two years later, we contacted the 22 participants who intended to manage notifications differently in the future. More than 75% of the participants had followed or followed partially through with their plans.

The fact that more than half of the participants reduced the number of notifications that they are exposed to on a daily basis is a warning sign that our participants were realizing a sense notification overload.

Conclusions

In conclusion, our results show strong and polarized reactions to being without notifications:

Notifications negatively impacted focused work, as participants reported to feel significantly less distracted and more productive without them. This is evidence that disabling notifications can have positive effects.

At the same time, disabling notifications also had significant negative effects: it made participants more worried to miss important information, not being responsive enough, and feeling less connected with their social network. Thus, disabling notifications altogether is not an option.

In contrast to a previous deprivation study, where all participants re-enabled work email notifications after the study, about one-third of our participants expressed the intention to disable some sources of notifications, and about half of our participants expressed the intention to use Do Not Disturb (and equivalent settings) more often in the future. Two years later, 60% of these participants are still following through with their intentions. Another 18% have changed their notification-related behavior.

Users often perceive ads annoying (when they are unrelated to their interests), or on the other hand they find ads creepy or scary (when matched to their interests and activities). Assuming the recommendation systems will become able to better and better match ads to our interests, how will the users react?

Many apps and internet services are free-to-use. The cost of developing and running these services is paid by bringing advertisement to our attention. However, ads are often annoying an unrelated to our interests.

Hence, services collect more and more information about us to better target and personalize those ads. While research shows that personalized ads can be less annoying and more effective, they require the logging of our demographics and our behavior, such as our searches, the content we browse, or our whereabouts and movements.

Personalization has raised concerns: users worry about the personal data that is being used to create personalized ads. In previous studies, when given a choice, many people expressed hesitation to share information: their concerns of sharing personal data –such as browsing and location history– outweigh the perceived usefulness of personalized ads.

If such concerns persist, investing effort into further personalizing ads would not be worthwhile, as people would not accept them.

We therefore set out to explore the research question: “would people be willing to share their personal data in exchange for highly-personalized online ads?”

To answer this question, we conducted a so called Wizard-of-Oz deception study, i.e., a study in which we simulated a system that can generate highly-personalized ads. Our volunteers were exposed via a web browser to three different highly-personalized ads, designed by people who knew them well. They were made believe that the ads had been generated automatically by an Artificial Intelligence engine on the basis of their browsing & location history and/or personal traits.

The participants’ reactions were surprisingly favorable:

in more than 50% of the cases, the ads triggered spontaneous positive emotional reactions;

almost 90% of participants would share at least two of the three data sources with advertisers; and

about 50% would share all data sources.

Our results provide evidence that highly-personalized ads may offset the concerns that people have about sharing their personal data. Thus further efforts in building increasingly personalized online ads would represent a worthwhile endeavor.

Guidelines often suggest 10, 12, or 14 points. 12 points is a number you see frequently mentioned in forums and blogs.

In 2009, Smashing Magazin found that a set of 50 very popular news pages used 13px on average. In 2013, they repeated the study and found that 14px and 16px had become the most popular font sizes. In 2015, Firefox and Chrome ship with a default of 16 pixels. However, guidelines and recommendations rarely cite scientific evidence.

Scientific research has been comparing font sizes 10, 12, 14 points and repeatedly found that bigger font implies better readability. This indicates that the current development of increasing font sizes in moving in the right direction. However, the larger the font becomes, the less text fits in one line. Is there a point where fonts become too large?

We were not able to find any scientific study that studies font sizes beyond 14 points. Thus, it was not clear to what extent increasing font sizes beyond 14 points improves readability.

We present results from the first, published scientific including font sizes 18, 22, and 26. In brief, the evidence suggest that readability keeps improving for larger fonts: 18 and 22 points.

In this study, 104 people read Wikipedia articles with different font sizes (10, 12, 14, 18, 22, 26 points – within-subject factor) and line spacings (0.8, 1.0, 1.4, 1.8 – between-group factor) while their reading was recorded with an eye-tracker.

From the eye-tracking data, we extracted the mean fixation duration of the eye movements, which is an established proxy for objective readability. When reading, the eye does not move continuously over the text. It alters between short fixations and saccades. The shorter those fixations are, the less difficulties the reader encounters, which means that the easier to text is to read.

In our data, the mean fixation duration dropped absolutely continuously with increasing font size until 18 points. The shortest mean fixatution durations were recorded for 22 points. Subjective readability was best for 18 point font size.

Further, for each text, the participants had to answer comprehension questions. The fraction of correctly answered comprehension questions was significantly lower for font sizes 10 and 12 points. This is impressive evidence about how small font sizes impair readability to an extent where comprehension gets affected.

In the study, we also tested different line spacings (0.8, 1.0, 1.4, and 1,8). However, line spacing had only minimal effects: we found weak evidence that extreme line spacings (0.8 and 1.8) may impair readability and comprehension.

In summary, our work supports recent calls for drastically increasing font size of website bodies. Our recommendation is to use 18 points font size and default (1.0) line spacing. Just to be clear, in a standard desktop setting, you need to set your Firefox or Chrome browser to 24 pixels to achieve this. In our data, this configuration strikes the balance between having the best readability, comprehension, subjective perception scores, and allowing to fit as much text on the screen as possible.

Of course, this recommendation does not consider aethetic aspects. It is simply about maximizing readability and comprehension of websites.

We might think that technology has solved the problem of boredom. More and more devices provide us with an ample source of entertainment at our fingertips.

Paradoxically, today we appear to be more prone to boredom than ever before. The explanation might be that over time people habituate to an increasing exposure to stimuli such that, when the level of stimulation drops, they become bored.

In an extension on our study of detecting phases of boredom from mobile phone usage, in this study, we (Aleksandar Matic, Nuria Oliver, and me of the Scientific Group of Telefonica) explored to what extent technology use is intertwined with boredom proneness, and whether the level of boredom proneness can be inferred from it. We collected data on the accumulated daily mobile phone usage patterns of 22 volunteers, such as, the average number of apps started in a day or the variance of the amount of notifications received per day. Then, those participants filled our the standardized Boredom Proneness Scale.

We found that daily usage patterns can estimate whether the person is above-average prone to boredom with an accuracy of over 80%. Individuals with high boredom proneness were having more unstable daily phone usage patterns: they launched a higher number of apps per day, had strong peaks of social network activity, and turned on the phone a lot. However, surprisingly, the overall time of using the phone was not higher than for individuals with lower boredom proneness.

Boredom proneness is related to a number of negative outcomes, such as depression, drug & alcohol consumption, or anxiety. Obtaining boredom proneness in an unobtrusive, automatic way can, amongst other things, help in the adjustment of the treatments of such health issues.

In times of information overload, attention has become a limiting factor in the way we consume information. Hence, researchers suggested to treat attention as a scarce resource coined the phrase attention economy. Given that attention is also what pays the bills of many free internet services through ads, some even speak of the Attention War. Soon, this war may start extending to our mobile devices, where already today, apps try to engage you through proactive push notifications.

Yet, attention is not always scarce. When being bored, attention is abundant, and people often turn to their phones to kill time. So, wouldn’t it be great if more services sought your attention when you are bored and left you alone when you were busy?

To identify, which usage patterns are indicative for boredom, we logged phone usage patterns of 54 volunteers for 2 weeks. At the same time, we asked them to frequently report how bored they felt. We found that patterns around the recency of communication activity, context, demographics, and phone usage intensity were related to boredom.

These patterns allow us to create a model that predicts when a person is more bored than usual with an AUCROC of 74.5%. It achieves a precision of over 62%, when its sensitivity is tuned detecting 50% of the boredom episodes.

While this is far from perfect, we proved its effectiveness in a follow-up study: we created an app (available on Google Play, more info here) that, at random times, created notifications, which suggest to read news articles.

When predicted bored, the participants opened those articles in over 20% of the cases and kept reading the article for more than 30 seconds in 15% of the cases. In contrast, when they were not bored, they opened the article in only 8% of the cases and kept reading it for more than 30 seconds in only 4% of the cases.
Statistical analysis shows that the predicting accounts for significant share of the observed increase.

While we certainly don’t feel that recommending Buzzfeed articles will be the cure peoples’ boredom, at least not for the majority of them, the study provides evidence that the prediction works.

Now how can mobile phones better serve users, when they can detect phases of boredom? We see four application scenarios:

Engage users with relevant contents to mitigate boredom,

Shield users from non-important interruptions when not bored,

Propose useful but not necessarily boredom-curing activities, such as clearing a backlog of To Do’s or revisiting vocabulary lists, and

Suggest to stop killing time with the phone and embrace boredom, as it is essential to creative processes and self-reflection.

The business model of many internet-service companies is primarily build around your attention: they offer best-in-class services for free in exchange for the users’ eyeballs, i.e. them paying attention to the contents of the services they offer. They pay for their expenses and generate revenue by selling the attracted attention to companies and individuals who’d like to promote their content.

In this battle, we may be facing the tragedy of the commons: when individual companies behave rationally according to their self-interest by increasing their attempts to seek people’s attention, they behave contrary to the best interests of the whole group by depleting the attentional resources of the user and risk that people develop notification blindness (as an analogy to banner blindness).

Attention is not always scarce

However, attention is not always scarce. For example, when people are bored, attention is abundant, and people often turn to their phones to kill time.

Boredom-Triggered Proactive Recommendations

This finding opens the door to using boredom as a content-independent trigger for proactive recommendations. Assuming that proactive recommendations delivered via mobile phone notifications will become more common in the future, using boredom as trigger will benefit service providers as well as the end users:
End users will receive fewer recommendations that are triggered during times when they are busy. Service providers can use it to reduce the fraction of unsuccessful recommendations, which, for example, decreases the likelihood that users develop notification blindness towards proactive recommendations.